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JACIII Vol.28 No.5 pp. 1164-1168
doi: 10.20965/jaciii.2024.p1164
(2024)

Research Paper:

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Verb Form Recognition and Error Detection in English Articles Using Long Short-Term Memory and Grammar Checks

Ping Hu* and Huicheng Zhang**,†

*Cangzhou Normal University
Cangzhou, Hebei 061001, China

**School of Foreign Languages, Cangzhou Jiaotong College
Huanghua , China

†Corresponding author

Received:
March 7, 2024
Accepted:
June 17, 2024
Published:
September 20, 2024
Creative Commons License
Keywords:
word form error detection, verb-form error detection, bidirectional long short-term memory, textual context utilization, parts-of-speech recognition
Abstract

Error checking of verb forms in English articles is beneficial for learning English and improving the fluency of English texts. In this study, long shortterm memory (LSTM) was used to recognize the types of errors in verb forms. To maximize the utilization of textual context information, a bidirectional LSTM algorithm was employed. Simulation experiments were then conducted, and the algorithm was evaluated against the support vector machine (SVM) algorithm and the grammar rules-based algorithm. The bidirectional LSTM method demonstrated higher accuracy in recognizing the parts of speech of words and the types of verb form errors in the text. Additionally, the accuracy was more stable when faced with different types of verb form errors.

Process of English verb form error recognition algorithm based on bidirectional long short-term memory

Process of English verb form error recognition algorithm based on bidirectional long short-term memory

Cite this article as:
P. Hu and H. Zhang, “Verb Form Recognition and Error Detection in English Articles Using Long Short-Term Memory and Grammar Checks,” J. Adv. Comput. Intell. Intell. Inform., Vol.28 No.5, pp. 1164-1168, 2024.
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Last updated on Oct. 11, 2024